Assessment of risk of ALS conferred by the GGGGCC hexanucleotide repeat expansion in C9orf72 among first-degree relatives of patients with ALS carrying the repeat expansion.

OBJECTIVES: We aimed to estimate the age-related risk of ALS in first-degree relatives of patients with ALS carrying the C9orf72 repeat expansion. METHODS: We included all patients with ALS carrying a C9orf72 repeat expansion in The Netherlands. Using structured questionnaires, we determined the number of first-degree relatives, their age at death due to ALS or another cause, or age at time of questionnaire. The cumulative incidence of ALS among first-degree relatives was estimated, while accounting for death from other causes. Variability in ALS risk between families was evaluated using a random effects hazards model. We used a second, distinct approach to estimate the risk of ALS and FTD in the general population, using previously published data. RESULTS: In total, 214 of the 2,486 (9.2%) patients with ALS carried the C9orf72 repeat expansion. The mean risk of ALS at age 80 for first-degree relatives carrying the repeat expansion was 24.1%, but ranged between individual families from 16.0 to 60.6%. Using the second approach, we found the risk of ALS and FTD combined was 28.7% (95% CI 17.8%-54.3%) for carriers in the general population. CONCLUSIONS: On average, our estimated risk of ALS in the C9orf72 repeat expansion was lower compared to historical estimates. We showed, however, that the risk of ALS likely varies between families and one overall penetrance estimate may not be sufficient to describe ALS risk. This warrants a tailor-made, patient-specific approach in testing. Further studies are needed to assess the risk of FTD in the C9orf72 repeat expansion.

Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart.

Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart, case study 1, needed to pool data from seven heterogeneous data sets: five randomized controlled trials from three different industry partners, and two disease registries. Sharing IPD was not considered feasible due to legal requirements and the sensitive medical nature of these data. In addition, harmonizing the data sets for a federated data analysis was difficult due to capacity constraints and the heterogeneity of the data sets. An alternative option was to share summary statistics through contingency tables. Here it is demonstrated that this method along with anonymization methods to ensure patient anonymity had minimal loss of information. Although sharing IPD should continue to be encouraged and strived for, our approach achieved a good balance between data transparency while protecting patient privacy. It also allowed a successful collaboration between industry and academia.

Identifying multivariate disease trajectories and potential phenotypes of early knee osteoarthritis in the CHECK cohort.

OBJECTIVE: To gain better understanding of osteoarthritis (OA) heterogeneity and its predictors for distinguishing OA phenotypes. This could provide the opportunity to tailor prevention and treatment strategies and thus improve care. DESIGN: Ten year follow-up data from CHECK (1002 early-OA subjects with first general practitioner visit for complaints ≤6 months before inclusion) was used. Data were collected on WOMAC (pain, function, stiffness), quantitative radiographic tibiofemoral (TF) OA characteristics, and semi-quantitative radiographic patellofemoral (PF) OA characteristics. Using functional data analysis, distinctive sets of trajectories were identified for WOMAC, TF and PF characteristics, based on model fit and clinical interpretation. The probabilities of knee membership to each trajectory were used in hierarchical cluster analyses to derive knee OA phenotypes. The number and composition of potential phenotypes was selected again based on model fit (silhouette score) and clinical interpretation. RESULTS: Five trajectories representing different constant levels or changing WOMAC scores were identified. For TF and PF OA, eight and six trajectories respectively were identified based on (changes in) joint space narrowing, osteophytes and sclerosis. Combining the probabilities of knees belonging to these different trajectories resulted in six clusters ('phenotypes') of knees with different degrees of functional (WOMAC) and radiographic (PF) parameters; TF parameters were found not to significantly contribute to clustering. Including baseline characteristics as well resulted in eight clusters of knees, dominated by sex, menopausal status and WOMAC scores, with only limited contribution of PF features. CONCLUSIONS: Several stable and progressive trajectories of OA symptoms and radiographic features were identified, resulting in phenotypes with relatively independent symptomatic and radiographic features. Sex and menopausal status may be especially important when phenotyping knee OA patients, while radiographic features contributed less. Possible phenotypes were identified that, after validation, could aid personalized treatments and patients selection.

Endometrial scratching in women undergoing IVF/ICSI: an individual participant data meta-analysis.

BACKGROUND: In IVF/ICSI treatment, the process of embryo implantation is the success rate-limiting step. Endometrial scratching has been suggested to improve this process, but it is unclear if this procedure increases the chance of implantation and live birth (LB) and, if so, for whom, and how the scratch should be performed. OBJECTIVE AND RATIONALE: This individual participant data meta-analysis (IPD-MA) aims to answer the question of whether endometrial scratching in women undergoing IVF/ICSI influences the chance of a LB, and whether this effect is different in specific subgroups of women. After its incidental discovery in 2000, endometrial scratching has been suggested to improve embryo implantation. Numerous randomized controlled trials (RCTs) have been conducted, showing contradicting results. Conventional meta-analyses were limited by high within- and between-study heterogeneity, small study samples, and a high risk of bias for many of the trials. Also, the data integrity of several trials have been questioned. Thus, despite numerous RCTs and a multitude of conventional meta-analyses, no conclusion on the clinical effectiveness of endometrial scratching could be drawn. An IPD-MA approach is able to overcome many of these problems because it allows for increased uniformity of outcome definitions, can filter out studies with data integrity concerns, enables a more precise estimation of the true treatment effect thanks to adjustment for participant characteristics and not having to make the assumptions necessary in conventional meta-analyses, and because it allows for subgroup analysis. SEARCH METHODS: A systematic literature search identified RCTs on endometrial scratching in women undergoing IVF/ICSI. Authors of eligible studies were invited to share original data for this IPD-MA. Studies were assessed for risk of bias (RoB) and integrity checks were performed. The primary outcome was LB, with a one-stage intention to treat (ITT) as the primary analysis. Secondary analyses included as treated (AT), and the subset of women that underwent an embryo transfer (AT+ET). Treatment-covariate interaction for specific participant characteristics was analyzed in AT+ET. OUTCOMES: Out of 37 published and 15 unpublished RCTs (7690 participants), 15 RCTs (14 published, one unpublished) shared data. After data integrity checks, we included 13 RCTs (12 published, one unpublished) representing 4112 participants. RoB was evaluated as 'low' for 10/13 RCTs. The one-stage ITT analysis for scratch versus no scratch/sham showed an improvement of LB rates (odds ratio (OR) 1.29 [95% CI 1.02-1.64]). AT, AT+ET, and low-RoB-sensitivity analyses yielded similar results (OR 1.22 [95% CI 0.96-1.54]; OR 1.25 [95% CI 0.99-1.57]; OR 1.26 [95% CI 1.03-1.55], respectively). Treatment-covariate interaction analysis showed no evidence of interaction with age, number of previous failed embryo transfers, treatment type, or infertility cause. WIDER IMPLICATIONS: This is the first meta-analysis based on IPD of more than 4000 participants, and it demonstrates that endometrial scratching may improve LB rates in women undergoing IVF/ICSI. Subgroup analysis for age, number of previous failed embryo transfers, treatment type, and infertility cause could not identify subgroups in which endometrial scratching performed better or worse. The timing of endometrial scratching may play a role in its effectiveness. The use of endometrial scratching in clinical practice should be considered with caution, meaning that patients should be properly counseled on the level of evidence and the uncertainties.

Sex differences in the generalizability of randomized clinical trials in heart failure with reduced ejection fraction.

AIMS: In order to understand how sex differences impact the generalizability of randomized clinical trials (RCTs) in patients with heart failure (HF) and reduced ejection fraction (HFrEF), we sought to compare clinical characteristics and clinical outcomes between RCTs and HF observational registries stratified by sex. METHODS AND RESULTS: Data from two HF registries and five HFrEF RCTs were used to create three subpopulations: one RCT population (n = 16 917; 21.7% females), registry patients eligible for RCT inclusion (n = 26 104; 31.8% females), and registry patients ineligible for RCT inclusion (n = 20 810; 30.2% females). Clinical endpoints included all-cause mortality, cardiovascular mortality, and first HF hospitalization at 1 year. Males and females were equally eligible for trial enrolment (56.9% of females and 55.1% of males in the registries). One-year mortality rates were 5.6%, 14.0%, and 28.6% for females and 6.9%, 10.7%, and 24.6% for males in the RCT, RCT-eligible, and RCT-ineligible groups, respectively. After adjusting for 11 HF prognostic variables, RCT females showed higher survival compared to RCT-eligible females (standardized mortality ratio [SMR] 0.72; 95% confidence interval [CI] 0.62-0.83), while RCT males showed higher adjusted mortality rates compared to RCT-eligible males (SMR 1.16; 95% CI 1.09-1.24). Similar results were also found for cardiovascular mortality (SMR 0.89; 95% CI 0.76-1.03 for females, SMR 1.43; 95% CI 1.33-1.53 for males). CONCLUSION: Generalizability of HFrEF RCTs differed substantially between the sexes, with females having lower trial participation and female trial participants having lower mortality rates compared to similar females in the registries, while males had higher than expected cardiovascular mortality rates in RCTs compared to similar males in registries.

Development and Evaluation of a Simulation-Based Algorithm to Optimize the Planning of Interim Analyses for Clinical Trials in ALS.

BACKGROUND AND OBJECTIVES: Late-phase clinical trials for neurodegenerative diseases have a low probability of success. In this study, we introduce an algorithm that optimizes the planning of interim analyses for clinical trials in amyotrophic lateral sclerosis (ALS) to better use the time and resources available and minimize the exposure of patients to ineffective or harmful drugs. METHODS: A simulation-based algorithm was developed to determine the optimal interim analysis scheme by integrating prior knowledge about the success rate of ALS clinical trials with drug-specific information obtained in early-phase studies. Interim analysis schemes were optimized by varying the number and timing of interim analyses, together with their decision rules about when to stop a trial. The algorithm was applied retrospectively to 3 clinical trials that investigated the efficacy of diaphragm pacing or ceftriaxone on survival in patients with ALS. Outcomes were additionally compared with conventional interim designs. RESULTS: We evaluated 183-1,351 unique interim analysis schemes for each trial. Application of the optimal designs correctly established lack of efficacy, would have concluded all studies 1.2-19.4 months earlier (reduction of 4.6%-57.7% in trial duration), and could have reduced the number of randomized patients by 1.7%-58.1%. By means of simulation, we illustrate the efficiency for other treatment scenarios. The optimized interim analysis schemes outperformed conventional interim designs in most scenarios. DISCUSSION: Our algorithm uses prior knowledge to determine the uncertainty of the expected treatment effect in ALS clinical trials and optimizes the planning of interim analyses. Improving futility monitoring in ALS could minimize the exposure of patients to ineffective or harmful treatments and result in significant ethical and efficiency gains.

Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validation.

BACKGROUND: The predictive capability of time-lapse monitoring (TLM) selection algorithms is influenced by patient characteristics, type and quality of data included in the analysis and the used statistical methods. Previous studies excluded DET cycles of which only one embryo implanted, introducing bias into the data. Therefore, we wanted to develop a TLM prediction model that is able to predict pregnancy chances after both single- and double embryo transfer (SET and DET). METHODS: This is a retrospective study of couples (n = 1770) undergoing an in vitro fertilization cycle at the Erasmus MC, University Medical Centre Rotterdam (clinic A) or the Reinier de Graaf Hospital (clinic B). This resulted in 2058 transferred embryos with time-lapse and pregnancy outcome information. For each dataset a prediction model was established by using the Embryo-Uterus statistical model with the number of gestational sacs as the outcome variable. This process was followed by cross-validation. RESULTS: Prediction model A (based on data of clinic A) included female age, t3-t2 and t5-t4, and model B (clinic B) included female age, t2, t3-t2 and t5-t4. Internal validation showed overfitting of model A (calibration slope 0.765 and area under the curve (AUC) 0.60), and minor overfitting of model B (slope 0.915 and AUC 0.65). External validation showed that model A was capable of predicting pregnancy in the dataset of clinic B with an AUC of 0.65 (95% CI: 0.61-0.69; slope 1.223, 95% CI: 0.903-1.561). Model B was less accurate in predicting pregnancy in the dataset of clinic A (AUC 0.60, 95% CI: 0.56-0.65; slope 0.671, 95% CI: 0.422-0.939). CONCLUSION: Our study demonstrates a novel approach to the development of a TLM prediction model by applying the EU statistical model. With further development and validation in clinical practice, our prediction model approach can aid in embryo selection and decision making for SET or DET.

Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.

Predicting pregnancy chances leading to term live birth in oligo/anovulatory women diagnosed with PCOS.

RESEARCH QUESTION: Which patient features predict the time to pregnancy (TTP) leading to term live birth in infertile women diagnosed with polycystic ovary syndrome (PCOS)? DESIGN: Prospective cohort follow-up study was completed, in which initial standardized phenotyping was conducted at two Dutch university medical centres from January 2004 to January 2014. Data were linked to the Netherlands Perinatal Registry to obtain pregnancy outcomes for each participant. All women underwent treatment according to a standardized protocol, starting with ovulation induction as first-line treatment. Predictors of pregnancies (leading to term live births) during the first year after PCOS diagnosis were evaluated. RESULTS: A total of 1779 consecutive women diagnosed with PCOS between January 2004 and January 2014 were included. In the first year following screening, 659 (37%) women with PCOS attained a pregnancy leading to term birth (≥37 weeks of gestational age). A higher chance of pregnancy was associated with race, smoking, body mass index (BMI), insulin, total testosterone and sex hormone-binding globulin (SHBG) concentrations (c-statistic = 0.59). CONCLUSIONS: Predictors of an increased chance of a live birth include White race, no current smoking, lower BMI, insulin and total testosterone concentrations, and higher SHBG concentrations. This study presents a nomogram to predict the chances of achieving a pregnancy (leading to a term live birth) within 1 year of treatment.

Prediction of Behavioral Improvement Through Resting-State Electroencephalography and Clinical Severity in a Randomized Controlled Trial Testing Bumetanide in Autism Spectrum Disorder.

BACKGROUND: Mechanism-based treatments such as bumetanide are being repurposed for autism spectrum disorder. We recently reported beneficial effects on repetitive behavioral symptoms that might be related to regulating excitation-inhibition (E/I) balance in the brain. Here, we tested the neurophysiological effects of bumetanide and the relationship to clinical outcome variability and investigated the potential for machine learning-based predictions of meaningful clinical improvement. METHODS: Using modified linear mixed models applied to intention-to-treat population, we analyzed E/I-sensitive electroencephalography (EEG) measures before and after 91 days of treatment in the double-blind, randomized, placebo-controlled Bumetanide in Autism Medication and Biomarker study. Resting-state EEG of 82 subjects out of 92 participants (7-15 years) were available. Alpha frequency band absolute and relative power, central frequency, long-range temporal correlations, and functional E/I ratio treatment effects were related to the Repetitive Behavior Scale-Revised (RBS-R) and the Social Responsiveness Scale 2 as clinical outcomes. RESULTS: We observed superior bumetanide effects on EEG, reflected in increased absolute and relative alpha power and functional E/I ratio and in decreased central frequency. Associations between EEG and clinical outcome change were restricted to subgroups with medium to high RBS-R improvement. Using machine learning, medium and high RBS-R improvement could be predicted by baseline RBS-R score and EEG measures with 80% and 92% accuracy, respectively. CONCLUSIONS: Bumetanide exerts neurophysiological effects related to clinical changes in more responsive subsets, in whom prediction of improvement was feasible through EEG and clinical measures.

Long-term effectiveness of dual CFTR modulator treatment of cystic fibrosis.

Background: Although short-term efficacy of lumacaftor/ivacaftor and tezacaftor/ivacaftor is clearly established in clinical trials, data on long-term effectiveness is limited. This registry-based cohort study assessed real-world longitudinal outcomes of F508del-homozygous people with cystic fibrosis (pwCF) ≥12 years, up to 3 years after the introduction of dual cystic fibrosis transmembrane conductance regulator (CFTR) modulators. Methods: Annual data (2010-2019) were retrieved from the Dutch Cystic Fibrosis Registry. Longitudinal trends of per cent predicted forced expiratory volume in 1 s (FEV1 % pred) decline, body mass index (BMI), BMI Z-score and intravenous antibiotic treatment duration before and after CFTR modulator initiation were assessed with linear and negative binomial mixed models. Results: We included 401 participants (41.9% female, baseline age 24.5 years (IQR 18.0-31.5 years), baseline mean±sd FEV1 70.5±23.4% pred). FEV1 decline improved from -1.36% pred per year to -0.48% pred per year after modulator initiation (change: 0.88% pred, 95% CI: 0.35-1.39%, p=0.001). This change was even 1.40% pred per year (95% CI: -0.0001-2.82%, p=0.050) higher in participants with baseline FEV1 <40% pred. In adults, annual BMI trend was not altered (change: 0.10 kg·m-2·year-1, 95% CI:-0.01-0.21, p=0.079). Annual BMI Z-score in children reversed from -0.08 per year before modulator treatment to 0.06 per year afterwards (change: 0.14 per year, 95% CI: 0.06-0.22, p<0.001). Intravenous antibiotic treatment duration showed a three-fold reduction in the first year after modulator initiation (incidence rate ratios (IRR): 0.28, 95% CI: 0.19-0.40, p<0.001), but the annual trend did not change in the subsequent years (IRR: 1.19, 95% CI: 0.94-1.50, p=0.153). Conclusion: Long-term effectiveness of dual CFTR modulator therapies on FEV1 decline, BMI and intravenous antibiotic treatment duration is less pronounced in a real-world setting than in clinical trials and varies considerably between pwCF and different baseline FEV1 levels.

Blood pressure and the risk of rebleeding and delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage.

INTRODUCTION AND OBJECTIVE: Blood pressure is presumably related to rebleeding and delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (aSAH) and could serve as a target to improve outcome. We assessed the associations between blood pressure and rebleeding or DCI in aSAH-patients. MATERIALS AND METHODS: In this observational study in 1167 aSAH-patients admitted to the intensive care unit (ICU), adjusted hazard ratio's (aHR) were calculated for the time-dependent association of blood pressure and rebleeding or DCI. The aHRs were presented graphically, relative to a reference mean arterial pressure (MAP) of 100 mmHg and systolic blood pressure (sBP) of 150 mmHg. RESULTS: A MAP below 100 mmHg in the 6, 3 and 1 h before each moment in time was associated with a decreased risk of rebleeding (e.g. within 6 h preceding rebleeding: MAP = 80 mmHg: aHR 0.30 (95% confidence interval (CI) 0.11-0.80)). A MAP below 60 mmHg in the 24 h before each moment in time was associated with an increased risk of DCI (e.g. MAP = 50 mmHg: aHR 2.59 (95% CI 1.12-5.96)). CONCLUSIONS: Our results suggest that a MAP below 100 mmHg is associated with decreased risk of rebleeding, and a MAP below 60 mmHg with increased risk of DCI.

Individual treatment effect estimation in the presence of unobserved confounding using proxies: a cohort study in stage III non-small cell lung cancer.

Randomized Controlled Trials (RCT) are the gold standard for estimating treatment effects but some important situations in cancer care require treatment effect estimates from observational data. We developed "Proxy based individual treatment effect modeling in cancer" (PROTECT) to estimate treatment effects from observational data when there are unobserved confounders, but proxy measurements of these confounders exist. We identified an unobserved confounder in observational cancer research: overall fitness. Proxy measurements of overall fitness exist like performance score, but the fitness as observed by the treating physician is unavailable for research. PROTECT reconstructs the distribution of the unobserved confounder based on these proxy measurements to estimate the treatment effect. PROTECT was applied to an observational cohort of 504 stage III non-small cell lung cancer (NSCLC) patients, treated with concurrent chemoradiation or sequential chemoradiation. Whereas conventional confounding adjustment methods seemed to overestimate the treatment effect, PROTECT provided credible treatment effect estimates.

Comparison of statistical methods for the analysis of recurrent adverse events in the presence of non-proportional hazards and unobserved heterogeneity: a simulation study.

BACKGROUND: In preventive drug trials such as intermittent preventive treatment for malaria prevention during pregnancy (IPTp), where there is repeated treatment administration, recurrence of adverse events (AEs) is expected. Challenges in modelling the risk of the AEs include accounting for time-to-AE and within-patient-correlation, beyond the conventional methods. The correlation comes from two sources; (a) individual patient unobserved heterogeneity (i.e. frailty) and (b) the dependence between AEs characterised by time-dependent treatment effects. Potential AE-dependence can be modelled via time-dependent treatment effects, event-specific baseline and event-specific random effect, while heterogeneity can be modelled via subject-specific random effect. Methods that can improve the estimation of both the unobserved heterogeneity and treatment effects can be useful in understanding the evolution of risk of AEs, especially in preventive trials where time-dependent treatment effect is expected. METHODS: Using both a simulation study and the Chloroquine for Malaria in Pregnancy (NCT01443130) trial data to demonstrate the application of the models, we investigated whether the lognormal shared frailty models with restricted cubic splines and non-proportional hazards (LSF-NPH) assumption can improve estimates for both frailty variance and treatment effect compared to the conventional inverse Gaussian shared frailty model with proportional hazard (ISF-PH), in the presence of time-dependent treatment effects and unobserved patient heterogeneity. We assessed the bias, precision gain and coverage probability of 95% confidence interval of the frailty variance estimates for the models under varying known unobserved heterogeneity, sample sizes and time-dependent effects. RESULTS: The ISF-PH model provided a better coverage probability of 95% confidence interval, less bias and less precise frailty variance estimates compared to the LSF-NPH models. The LSF-NPH models yielded unbiased hazard ratio estimates at the expense of imprecision and high mean square error compared to the ISF-PH model. CONCLUSION: The choice of the shared frailty model for the recurrent AEs analysis should be driven by the study objective. Using the LSF-NPH models is appropriate if unbiased hazard ratio estimation is of primary interest in the presence of time-dependent treatment effects. However, ISF-PH model is appropriate if unbiased frailty variance estimation is of primary interest. TRIAL REGISTRATION: ClinicalTrials.gov; NCT01443130.

Forskolin-induced organoid swelling is associated with long-term cystic fibrosis disease progression.

RATIONALE: Cystic fibrosis (CF) is a monogenic life-shortening disease associated with highly variable individual disease progression which is difficult to predict. Here we assessed the association of forskolin-induced swelling (FIS) of patient-derived organoids with long-term CF disease progression in multiple organs and compared FIS with the golden standard biomarker sweat chloride concentration (SCC). METHODS: We retrieved 9-year longitudinal clinical data from the Dutch CF Registry of 173 people with mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Individual CFTR function was defined by FIS, measured as the relative size increase of intestinal organoids after stimulation with 0.8 µM forskolin, quantified as area under the curve (AUC). We used linear mixed-effect models and multivariable logistic regression to estimate the association of FIS with long-term forced expiratory volume in 1 s % predicted (FEV1pp) decline and development of pancreatic insufficiency, CF-related liver disease and diabetes. Within these models, FIS was compared with SCC. RESULTS: FIS was strongly associated with longitudinal changes of lung function, with an estimated difference in annual FEV1pp decline of 0.32% (95% CI 0.11-0.54%; p=0.004) per 1000-point change in AUC. Moreover, increasing FIS levels were associated with lower odds of developing pancreatic insufficiency (adjusted OR 0.18, 95% CI 0.07-0.46; p<0.001), CF-related liver disease (adjusted OR 0.18, 95% CI 0.06-0.54; p=0.002) and diabetes (adjusted OR 0.34, 95% CI 0.12-0.97; p=0.044). These associations were absent for SCC. CONCLUSION: This study exemplifies the prognostic value of a patient-derived organoid-based biomarker within a clinical setting, which is especially important for people carrying rare CFTR mutations with unclear clinical consequences.

Generalizability of randomized controlled trials in heart failure with reduced ejection fraction.

BACKGROUND: Heart failure (HF) trials have stringent inclusion and exclusion criteria, but limited data exist regarding generalizability of trials. We compared patient characteristics and outcomes between patients with HF and reduced ejection fraction (HFrEF) in trials and observational registries. METHODS AND RESULTS: Individual patient data for 16 922 patients from five randomized clinical trials and 46 914 patients from two HF registries were included. The registry patients were categorized into trial-eligible and non-eligible groups using the most commonly used inclusion and exclusion criteria. A total of 26 104 (56%) registry patients fulfilled the eligibility criteria. Unadjusted all-cause mortality rates at 1 year were lowest in the trial population (7%), followed by trial-eligible patients (12%) and trial-non-eligible registry patients (26%). After adjustment for age and sex, all-cause mortality rates were similar between trial participants and trial-eligible registry patients [standardized mortality ratio (SMR) 0.97; 95% confidence interval (CI) 0.92-1.03] but cardiovascular mortality was higher in trial participants (SMR 1.19; 1.12-1.27). After full case-mix adjustment, the SMR for cardiovascular mortality remained higher in the trials at 1.28 (1.20-1.37) compared to RCT-eligible registry patients. CONCLUSION: In contemporary HF registries, over half of HFrEF patients would have been eligible for trial enrolment. Crude clinical event rates were lower in the trials, but, after adjustment for case-mix, trial participants had similar rates of survival as registries. Despite this, they had about 30% higher cardiovascular mortality rates. Age and sex were the main drivers of differences in clinical outcomes between HF trials and observational HF registries.

Innovating Clinical Trials for Amyotrophic Lateral Sclerosis: Challenging the Established Order.

Development of effective treatments for amyotrophic lateral sclerosis (ALS) has been hampered by disease heterogeneity, a limited understanding of underlying pathophysiology, and methodologic design challenges. We have evaluated 2 major themes in the design of pivotal, phase 3 clinical trials for ALS-(1) patient selection and (2) analytical strategy-and discussed potential solutions with the European Medicines Agency. Several design considerations were assessed using data from 5 placebo-controlled clinical trials (n = 988), 4 population-based cohorts (n = 5,100), and 2,436 placebo-allocated patients from the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database. The validity of each proposed design modification was confirmed by means of simulation and illustrated for a hypothetical setting. Compared to classical trial design, the proposed design modifications reduce the sample size by 30.5% and placebo exposure time by 35.4%. By making use of prognostic survival models, one creates a potential to include a larger proportion of the population and maximize generalizability. We propose a flexible design framework that naturally adapts the trial duration when inaccurate assumptions are made at the design stage, such as enrollment or survival rate. In case of futility, the follow-up time is shortened and patient exposure to ineffective treatments or placebo is minimized. For diseases such as ALS, optimizing the use of resources, widening eligibility criteria, and minimizing exposure to futile treatments and placebo is critical to the development of effective treatments. Our proposed design modifications could circumvent important pitfalls and may serve as a blueprint for future clinical trials in this population.

Developing more generalizable prediction models from pooled studies and large clustered data sets.

Prediction models often yield inaccurate predictions for new individuals. Large data sets from pooled studies or electronic healthcare records may alleviate this with an increased sample size and variability in sample characteristics. However, existing strategies for prediction model development generally do not account for heterogeneity in predictor-outcome associations between different settings and populations. This limits the generalizability of developed models (even from large, combined, clustered data sets) and necessitates local revisions. We aim to develop methodology for producing prediction models that require less tailoring to different settings and populations. We adopt internal-external cross-validation to assess and reduce heterogeneity in models' predictive performance during the development. We propose a predictor selection algorithm that optimizes the (weighted) average performance while minimizing its variability across the hold-out clusters (or studies). Predictors are added iteratively until the estimated generalizability is optimized. We illustrate this by developing a model for predicting the risk of atrial fibrillation and updating an existing one for diagnosing deep vein thrombosis, using individual participant data from 20 cohorts (N = 10 873) and 11 diagnostic studies (N = 10 014), respectively. Meta-analysis of calibration and discrimination performance in each hold-out cluster shows that trade-offs between average and heterogeneity of performance occurred. Our methodology enables the assessment of heterogeneity of prediction model performance during model development in multiple or clustered data sets, thereby informing researchers on predictor selection to improve the generalizability to different settings and populations, and reduce the need for model tailoring. Our methodology has been implemented in the R package metamisc.

An old friend who has overstayed their welcome: the ALSFRS-R total score as primary endpoint for ALS clinical trials.

Objective: The ALSFRS-R is limited by multidimensionality, which originates from the summation of various subscales. This prevents a direct comparison between patients with identical total scores. We aim to evaluate how multidimensionality affects the performance of the ALSFRS-R in clinical trials. Methods: We simulated clinical trial data with different treatment effects for the ALSFRS-R total score and its subscales (i.e. bulbar, fine motor, gross motor and respiratory). We considered scenarios where treatment reduced the rate of ALSFRS-R subscale decline either uniformly (i.e. all subscales respond identically to treatment) or non-uniformly (i.e. subscales respond differently to treatment). Two main analytical strategies were compared: (1) analyzing only the total score or (2) utilizing a subscale-based test (i.e. alternative strategy). For each analytical strategy, we calculated the empirical power and required sample size. Results: Both strategies are valid when there is no treatment benefit and provide adequate control of type 1 error. If all subscales respond identically to treatment, using the total score is the most powerful approach. As the differences in treatment responses between subscales increase, the more the total score becomes affected. For example, to detect a 40% reduction in the bulbar rate of decline with 80% power, the total score requires 1380 patients, whereas this is 336 when using the alternative strategy. Conclusions: Ignoring the multidimensional structure of the ALSFRS-R total score could have negative consequences for ALS clinical trials. We propose determining treatment benefit on a subscale level, prior to stating whether a treatment is generally effective.

A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data.

The use of smartphone-based location data to quantify behavior longitudinally and passively is rapidly gaining traction in neuropsychiatric research. However, a standardized and validated preprocessing framework for deriving behavioral phenotypes from smartphone-based location data is currently lacking. Here, we present a preprocessing framework consisting of methods that are validated in the context of geospatial data. This framework aims to generate context-enriched location data by identifying stationary, non-stationary, and recurrent stationary states in movement patterns. Subsequently, this context-enriched data is used to derive a series of behavioral phenotypes that are related to movement. By using smartphone-based location data collected from 245 subjects, including patients with schizophrenia, we show that the proposed framework is effective and accurate in generating context-enriched location data. This data was subsequently used to derive behavioral readouts that were sensitive in detecting behavioral nuances related to schizophrenia and aging, such as the time spent at home and the number of unique places visited. Overall, our results indicate that the proposed framework reliably preprocesses raw smartphone-based location data in such a manner that relevant behavioral phenotypes of interest can be derived.